code: def simulate_differential_accessibility(
      pos_motif_names,
      neg_motif_names,
      seq_length,
      min_num_motifs,
      max_num_motifs,
      num_pos,
      num_neg,
      GC_fraction,
  ):
      """Generates data for differential accessibility task.
  
      Parameters
      ----------
      pos_motif_names : list
          List of strings.
      neg_motif_names : list
          List of strings.
      seq_length : int
      min_num_motifs : int
      max_num_motifs : int
      num_pos : int
      num_neg : int
      GC_fraction : float
  
      Returns
      -------
      sequence_arr : 1darray
          Contains sequence strings.
      y : 1darray
          Contains labels.
      embedding_arr: 1darray
          Array of embedding objects.
      """
      (
          pos_motif_sequence_arr,
          _,
          positive_embedding_arr,
      ) = simulate_multi_motif_embedding(
          pos_motif_names,
          seq_length,
          min_num_motifs,
          max_num_motifs,
          num_pos,
          GC_fraction,
      )
      (
          neg_motif_sequence_arr,
          _,
          negative_embedding_arr,
      ) = simulate_multi_motif_embedding(
          neg_motif_names,
          seq_length,
          min_num_motifs,
          max_num_motifs,
          num_neg,
          GC_fraction,
      )
      sequence_arr = np.concatenate(
          (pos_motif_sequence_arr, neg_motif_sequence_arr)
      )
      y = np.array([[True]] * num_pos + [[False]] * num_neg)
      embedding_arr = positive_embedding_arr + negative_embedding_arr
      return sequence_arr, y, embedding_arr
  
file_path: deepchem/deepchem/molnet/dnasim.py
imports: import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: simulate_differential_accessibility(pos_motif_names, neg_motif_names, seq_length, min_num_motifs, max_num_motifs, num_pos, num_neg, GC_fraction) and returns: sequence_arr,  y,  embedding_arr